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Course Outline
Introduction to Artificial Intelligence
- Defining AI and identifying its applications
- Distinguishing between AI, Machine Learning, and Deep Learning
- Overview of prevalent tools and platforms
Python for AI
- Review of Python fundamentals
- Utilizing Jupyter Notebook
- Managing and installing necessary libraries
Data Processing
- Data preparation and cleaning workflows
- Leveraging Pandas and NumPy
- Data visualization using Matplotlib and Seaborn
Fundamentals of Machine Learning
- Comparison of Supervised and Unsupervised Learning
- Techniques for classification, regression, and clustering
- Processes for model training, validation, and testing
Neural Networks and Deep Learning
- Understanding neural network architecture
- Application of TensorFlow or PyTorch
- Constructing and training models
Natural Language Processing and Computer Vision
- Text classification and sentiment analysis methods
- Basics of image recognition
- Utilization of pre-trained models and transfer learning
AI Deployment in Applications
- Strategies for saving and loading models
- Integrating AI models into APIs or web applications
- Best practices for testing and ongoing maintenance
Summary and Next Steps
Requirements
- A solid grasp of programming logic and structures
- Proficiency with Python or equivalent high-level programming languages
- Foundational knowledge of algorithms and data structures
Audience
- IT systems professionals
- Software developers interested in integrating AI capabilities
- Engineers and technical managers exploring AI-driven solutions
40 Hours
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
